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The isetL3 repository software implements work described in this patent: Learning of Image Processing Pipeline for Digital Imaging Devices and this published paper

Learning the Image Processing Pipeline (2017). 
H. Jiang, Q. Tian, J. E. Farrell, B. Wandell
IEEE Transactions on Image Processing
Volume 10, pages 5032 - 5042

Abstract

"Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image processing pipeline that transforms the sensor data into a form, that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications. (Jiang et al., 2017)"

Talk on L3

You may be interested to see a talk presenting the ideas and their applications, but you will need to have a Stanford login or be a SCIEN affiliate. If you would like a temporary login to see the talk, please contact wandell@stanford.edu.